Objectives Entry into employment may be a time when a young person’s well-being and mental health is challenged. Specifically, we examined the difference in mental health when a young person was “not in the labor force” (NILF) (ie, non-working activity such as participating in education) compared to being in a job with varying levels of psychosocial quality.

Method The data source for this study was the Household Income and Labor Dynamics in Australia (HILDA) study, and the sample included 10 534 young people (aged ≤30 years). We used longitudinal fixed-effects regression to investigate within-person changes in mental health comparing circumstances where individuals were NILF to when they were employed in jobs of varying psychosocial quality.

Results Compared to when individuals were not in the labor force, results suggest a statistically significant decline in mental health when young people were employed in jobs with poor psychosocial working conditions and an improvement in mental health when they were employed in jobs with optimal psychosocial working conditions. Our results were robust to various sensitivity tests, including adjustment for life events and the lagged effects of mental health and job stressors.

Conclusions If causal, the results suggest that improving the psychosocial quality of work for younger workers will protect and promote their wellbeing, and may reduce the likelihood of mental health problems later on.

Adolescence and early adulthood is the peak age of onset for many mental disorders, with 75% of lifetime cases of mental illness
having their first onset by age 24 (1). However, people in this age group are less likely than others to seek professional help (2). This is problematic because early-age onset of mental disorder is associated with a longer duration of untreated illness
and poorer long-term outcomes (3).

The development of a mental health problem also impairs participation in the labor force (4). However, evidence suggests that this relationship is likely to be bidirectional, whereby not participating in work contributes
to mental health problems and vice versa (5). Across all age groups, research is also emerging that poor quality employment is worse for mental health than having no
job at all (6, 7). Thus, poor quality jobs may damage mental health even to the extent that a person may leave employment all together, while
high quality jobs may promote mental health and wellbeing as well as workforce engagement. High quality work promotes positive
interactions with others (colleagues, supervisors, etc) and skill development and learning, as well providing benefits such
as pay and security (8). These factors have been found to be associated with greater job satisfaction among young workers, which in turn, is related
to a reduced likelihood of them leaving the workforce (9).

Currently, young people in Australia aged 15–24 years comprise about 30% of all employed persons (10), with a proportion of these people also studying, and being employed in part time and casual work. Younger workers may face
a number of challenges when entering the workforce (11). A recent study analyzing the Household Income and Labor Dynamics in Australia (HILDA) cohort showed that younger workers
consistently report lower job control than their older counterparts (12). Earlier working population-based studies have also shown higher prevalence of job strain (low control jobs with high psychological
demands), higher prevalence of unwanted sexual advances at work, and higher prevalence of casual and temporary employment
among younger workers (13–16). Other studies have shown that young workers are particularly vulnerable to bullying and conflict with supervisors and colleagues
(17) and perceive greater inequity in their treatment at work compared to other workers (18). There is also some evidence that the adverse employment circumstances young people find themselves in are associated with
the risk of depression or anxiety (19). Further, early adversities experienced at work may have negative effects on well-being and depressive symptoms years after
they are experienced (20). Conversely, positive early experiences at work can present an opportunity for young people to develop resilience and the
ability to adapt to challenges at work (20, 21). As mentioned above, positive experiences in the workplace – such as support from colleagues and supervisors and the opportunity
for skill development and learning – have been found to predict greater retention of young people in the workforce (9).

Recently, we published an article using an existing longitudinal data source showing that the relationship between job stressors
and mental health is mainly contemporaneous (22). In the present article, we specifically focus on young workers in the same cohort to examine the impact of young people’s
entry into paid work on their mental health. We were specifically interested in the difference in mental health when a person
moved into a job with an optimal psychosocial environment, and the difference in mental health when a person moved into a
job with a poor psychosocial work environment.

Hypotheses

We hypothesized that young persons (≤30 years) in jobs with high psychosocial job quality will have an improvement in mental
health compared to when they are not in employment, while young persons in work with poor psychosocial job quality will experience
a decline in mental health compared to when they are not in employment. A cut-off of 30 years of age was chosen given that
young people may be undertaking tertiary education well into their mid or late 20s.

Methods

Data source

The HILDA survey is a longitudinal, nationally representative study of Australian households established in 2001, with 13
years of data currently available for analysis. The first wave collected detailed information from >13 000 individuals within
>7000 households (23). The response rate to wave 1 was 66% (23). The survey covers a range of dimensions including social, demographic, health, and economic conditions using a combination
of face-to-face interviews with trained interviewers and a self-reporting questionnaire. Although data are collected on each
member of the household, interviews are only conducted with those >15 years of age.

The initial wave of the survey began with a large national probability sample of Australian households occupying private dwellings
(23). Interviews were sought in later waves with all persons in sample households who had attained 15 years of age. Additional
persons have been added to the sample as a result of changes in household composition with a top-up sample of 2000 people
added to the cohort in 2011 to allow better representation of the Australian population using the same methodology as the
original sample (ie, a three-stage area-based design) (24). The response rates for new respondents who join the HILDA survey are >70% and the (wave-to-wave) retention rate for respondents
who continue in the survey is >90% (23). The Australian Department of Social Services approved this study.

The analytic sample can be seen in Figure 1. We first selected people <30 years of age only. Following this, people who had information on mental health and psychosocial
job quality and other covariates were selected (described further below).

Figure 1

Analytic sample.

Outcome variable: mental health

The Mental Component Summary (MCS) of the Short Form 36 (SF-36) measure was used as the primary outcome measure. The SF-36
is a widely used self-completion measure of health status and has been validated for use in the Australian population and
to detect within-person change over time (25). The MCS score represents a summary measure of mental health and well-being constructed from the eight subscales but with
strongest factor loadings on the mental health, vitality, and emotional and social role functioning scales (25). Thus, this is an integrated measure of overall mental health, rather than a scale measuring clinical factors. The SF-36
in the HILDA survey has been shown to be psychometrically sound, with good internal consistency, discriminant validity, and
high reliability (25). The mean score on the MCS in HILDA was approximately 49.8, with a standard deviation (SD) of 10.3. Higher scores represent
better mental health. The range of the MCS is 1–100, with 100 representing optimal functioning. All of the SF-36 scales demonstrated
acceptable internal consistency, with Cronbach’s alpha ranging from 0.82 (mental and general health) to 0.93 (physical functioning).
These reliability scores are similar to those reported in previous Australian research (25).

Main exposure variable: psychosocial job quality

A multidimensional measure of psychosocial job quality was constructed using the measures of psychosocial job characteristics
available in the HILDA survey (job control, job demands and complexity, job insecurity, and unfair pay). Full details of the
construction and validation of the job quality measure are presented elsewhere (6, 26, 27) and the measure is strongly related to widely used measures of job demands and control (6). In brief, factor analysis and structural equation modelling identified three separate factors: job demands and complexity
(three items); job control (three items); and perceived job security (three items). An additional single item assessing whether
respondents considered that they were paid fairly for their efforts at work was included as a fourth factor measuring an important
aspect of the effort–reward imbalance model (28). The individual scales were associated with more widely used measures of job demands and control, and other employment conditions
such as casual status, hours worked and shift work. Each factor was dichotomized to identify the quartile experiencing the
greatest adversity and the composite measure constructed by summing the number of adverse psychosocial job conditions (high
job demands and complexity, low job control, high job insecurity and unfair pay). Because of the small number of respondents
reporting all four job adversities in a single year/wave, this composite scale was top-coded at three and, thus, produced
four categories ranging from optimal jobs to ≥3 psychosocial adversities (poorest quality jobs). This measure has been used
in other studies on mental health (6), physical health (26), and sickness absence (29). Our reference category was “not in the labor force” (NILF). Undertaking education was the main reason for being NILF in
the sample.

Other covariates

We include time-varying potential confounders in regression models and descriptive tables: age (measured continuously); highest
level of education (postgraduate, bachelor, certificate or diploma, year 12, less than year 12); presence of disability or
long term health condition (yes/no) and household structure (couple or single adult residing with dependents, couple without
dependents, single person without dependents, and a group or multiple person household), and household equalized income. Household
equalized household income is an indicator of the economic resources available to a standardized household. Values are centered
around the mean income per year, and divided by AUS$10 000. We also considered the following life events as possible risk
factors for changes in mental health in sensitivity analysis: separated from spouse; got married/got back together with spouse;
self/close family member went to jail; birth/pregnancy; death of a close friend/relative or family member/spouse or child.

Analytical approach

Longitudinal linear fixed-effects regression models were used to estimate the association between psychosocial job quality
(exposure) and mental health (outcome). Fixed effect models show that MCS for the ith of N individuals is predicted by time-varying psychosocial job quality (optimal, 1 adversity, 2 adversities, ≥3 adversities,
compared to the reference NILF) (β1JobQualityit) and time-varying covariate (Xit). In equation A, μi refers to the unit-specific error term (eg, person-specific error term) that differs between persons, whereas εit is the error term associated with all regression models (eg, varies across individuals and over time) (30, 31). The term μi is included in the formula because it allows researchers to explicitly state that the persons-units are a source of error
and controlled in the model as well as normal sources of error that vary across time and person (εit).

Equation A. Fixed-effect model

MCSit=β0+β1JobQualityit+β2Xit+μi+εit

Fixed-effect analysis takes the mean of the observations when a person was “exposed” to NILF (eg, the years when a person
was not employed) over time, and compares these the mean of observations when a person was employed in poor/good psychosocial
job quality over time. Hence, these models provide an indication of within-person effects, where each individual acts as their
own control and estimates are not confounded by personal, demographic and environmental factors that do not change over time
(time-invariant) (32). Fixed-effects models are particularly useful where time-invariant confounding is likely to cause bias in causal estimates.
For example, both mental health and perceived psychosocial working conditions may be affected by within-person factors such
as personality, early childhood experiences, or medical history (each of which are time invariant in the analyses conducted).
As mentioned above, we controlled for time-varying (or variant) confounding in equation A by including a number of relevant
covariates (age, household structure, health status, and education) in the fixed-effects models. Each variable in the analysis
was available from 2001–2013.

With respect to the time between exposure and outcome, psychosocial job quality and mental health were analysed in the same
year. This is based on evidence from a previous panel study of four annual waves showing that changes in job stressors were
associated with changes in mental health over a one-year time frame (33) as well as previous analyses in the HILDA dataset showing that most of the effect of job stressors on scaled measures of
mental health was contemporaneous (22, 34).

We conducted a sensitivity analysis excluding those who were still in part- or full-time education. The rationale for this
was that those who were still studying while also working may be less psychologically invested in their jobs than those not
studying, and thus be less exposed to or concerned about what was happening in the workplace. We then conducted a sensitivity
analysis including life events as possible confounders as well a further analysis assessing the relationship between each
of the four psychosocial job stressors in the psychosocial job quality scale with mental health. This provided information
about the extent to which results were driven by specific job stressors. Last, we assessed the impact of lagged mental health
and job stressors on mental health using an Arellano-Bond model. As we have previously described (22), this model uses the first-difference model and applies a generalized method of moments (GMM) estimator where earlier lagged
values of the explanatory and outcome variables are used as instrumental variables for the lagged change in the outcome variable.
Analysis was conducted using Stata 14.1 (StataCorp, College Station, TX, USA).

Results

Table 1 describes the frequency of persons and observations in each of the employment states while table 2 shows the key demographics of the sample. We include summary measures from each individual’s first and last contributed waves
(not necessarily same calendar years) in HILDA to describe how the sample changes over time. The average age at the entry
to the study was 20.5 years, and the average age at the last recorded observation was 24 years. The income in the initial
wave was approximately AUS$36 500, and this rose to approximately AUS$44 000 in the final wave. There were equal numbers of
men and women in the sample, and this remained consistent over time. Household structure changed over time, with an increase
in couple and single-person households. This probably reflects the shift from young people living with their family (“couple
with children”) to on their own or with others. There was an increase in the proportion of people employed from 60.6% to 71.6%,
and a corresponding reduction in those who were NILF, falling from 29% to 20%. Of those who were employed, there was an increase
in permanent jobs (45% to 55%) and a decrease in casual jobs (42% to 30%). Those in high-skill occupations also increased
(21% to 28%). Participants in the sample were slightly more likely to move into optimal (no adversities such as low control,
high demands, low security, and unfair pay) work (26% to 27%) and less likely to move into work with adversities over the
course of the study. For the entire sample, education levels increased over time, with the proportion of observations reporting
a certificate/diploma rising from 18% to 25%, and bachelor degrees from 10% to 15%. The presence of long-term health conditions/disability
was relatively stable, with only a 0.7% increase in final waves reporting the presence of a health issue.

Table 1

Frequency of NILF (“not in the labor force”) and employment by psychosocial job quality, people, and observations. The sample
only included those aged ≤30 years.

People

Observations

Not in the labor force

5018

11 189

Psychosocial job quality (number of adversities)

0

4247

8393

1

7624

18 627

2

4227

7082

≥3

1800

2449

Table 2

Description of key demographics and employment characteristics of persons in the analytic sample. [SD=standard deviation.]

a Percentage of those employed; the sample only included those aged ≤30 years. Based on 10 534 individuals with 39 761 observations.

Table 3 shows descriptive results for overall mean levels of mental health associated with different employment states (average of
all contributed waves in that state) and, among the employed, the overall mean score of mental health associated with being
in jobs with different levels of psychosocial job stressors. Overall, the greatest disparities in mental health were found
in relation to psychosocial job quality. Compared to those employed in jobs with optimal psychosocial working conditions,
people working in a job with ≥3 adversities report levels of mental health close to six points lower. It should be noted that
this descriptive analysis pools across people and time, and therefore does not provide information on within-individual effects.

Table 3

Mean and standard deviation (SD) of mental health score by selected employment and individual characteristics. a

Mean

SD

Employment type

Employed

48.20

9.72

Not in labor force

46.30

11.60

Psychosocial job quality (number of adversities)

0

50.06

8.69

1

48.48

9.55

2

46.73

10.06

≥3

44.48

11.16

Gender

Male

49.18

9.55

Female

46.49

10.62

Age

<20

48.10

10.25

20–25

47.48

10.16

26–30

47.73

10.25

Occupational skill level

Low

48.14

10.01

Medium

48.08

9.80

High

48.47

9.15

a Numbers in this descriptive table are pooled across people and time, and therefore do not provide information on within
individual effects. Based on 10 534 individuals with 39 761 observations.

Table 4 shows the results of the longitudinal fixed-effects (within-persons) regression analyses, where we compared the average effects
of being out of the labor force (eg, in education) to employment in jobs with optimal versus suboptimal psychosocial working
conditions. The multivariate results show that, compared to when they are not in work (eg, education or the period just following
school), being in optimal employment is associated with a slight improvement in mental health within persons. In comparison,
there is a stepwise decrease in mental health when a person was employed in a job with ≥2 adversities. There was no statistical
significant difference for individuals who moved from not being in the labor force to jobs with 1 adversity.

Results also show a slight decrease in mental health as people aged closer to 30 years (the upper age limit of the sample)
and for those living without a partner or in a mixed household (eg, with those that are not family) compared to living as
part of a couple. Those without long-term health conditions had significantly better mental health compared to when they reported
a long-term health condition.

We conducted a further analysis removing those who were still studying while also working and found similar effects (supplementary
table A, www.sjweh.fi/index.php?page=data-repository). Another analysis excluding NILF waves showed a clearer stepwise pattern between declining psychosocial job quality and
declining mental health (supplementary table B, www.sjweh.fi/index.php?page=data-repository). Our sensitivity analysis including life events as possible confounders did not influence the relationship between employment
and mental health (supplementary table C, www.sjweh.fi/index.php?page=data-repository). Further, results of the Arellano-Bond modelling can be seen in supplementary table D, www.sjweh.fi/index.php?page=data-repository). Please note that the sample is smaller (5240 people, 17 861 observations) than in the models in the paper proper due to
the restrictions necessary to perform this analytic procedure, limiting generalizability and power. This likely explains the
fact that some of the results fall out of significance. Although, we would note that all the results are in the same direction.
As can be seen, there is a small effect of lagged mental health. Further analysis revealed that job insecurity (coefficient
-1.43, 95% CI -1.69– -1.17, P<0.001) was associated with the greatest decline in mental health for young people, followed
by low fairness of pay (coefficient-1.17, 95% CI -1.42– -0.92, P<0.001), high job demands (coefficient -0.48, 95% CI -0.73–
-0.23, P<0.001), and low job control (coefficient -0.77, 95% CI -1.02– -0.51, P<0.001).

Discussion

We observed declines in mental health for people in jobs with ≥2 psychosocial adversities (low control, high demands, low
security, and unfair pay) compared to when individuals were not in the labor force, while young people entering into high
psychosocial quality work had a modest improvement in mental health. Put another way, these results indicate that young people
working in poor psychosocial quality jobs may experience a small but statistically significant decline in mental health relative
to when they were not in the labor force, but when in jobs with high psychosocial job quality, they experience an improvement
in mental health. This suggests that promoting high quality psychosocial work for younger workers will protect and promote
their well-being and may reduce the likelihood of later mental health problems, particularly if this sets the young person
up for a working life characterized by good psychosocial quality jobs.

As the MCS of the SF-36 is not a clinical measure, it is difficult to draw conclusions regarding clinical significance. However,
we would note that a difference of three points on one of the most dominant subscales (the 5-item Mental Health Inventory
[MHI], which primarily assesses symptoms of depression and anxiety) has been suggested to reflect a minimally important difference
(34) and a difference of four or more on the unstandardized scale has been characterized as indicating a moderately clinically
significant effect (35). The MHI has reasonable validity and is an effective screening instrument for mood disorders or severe depressive symptomatology
in the general population (36–39). The difference across levels of psychosocial job quality observed in our study was relatively small (2–3 points). Nevertheless,
when combined with the observed stepwise dose–response by levels of psychosocial job quality, this suggests a causal relationship
between psychosocial job quality and mental health among young workers.

There is limited quantitative research internationally with which to compare our findings on the experience of young people
going into paid work. One of few we were able to find was a Swiss study on young adults entering the workforce after vocational
training into five different occupational groups (21). Results suggest the factors that contributed to well-being among younger workers included improved job control and feeling
appreciated at work (21). Data from the Queensland-based Young Workers Advisory Service (YWAS) in 2007 showed that young workers frequently seek
help from the YWAS for three main reasons: (i) low level of pay and conditions (pay/remuneration); (ii) a high level of precariousness
in employment (dismissal/redundancy), and; (iii) a high level of vulnerability to exploitation (employment conditions) (18). Two further areas of concern included the low quality of many young workers’ jobs (including their lack of access to training
and skills upgrading) and workplace bullying, which constituted one-fifth of all employment-related concerns reported to YWAS.
These findings are consistent with previous research showing that jobs with high job strain (low control combined with high
demands) have an adverse effect on job-related learning (40) as well as our previous research that younger workers have lower levels of job control than their older counterparts (12).

Our research also extends previous Australian research on psychosocial job quality and mental health (6, 41), in particular strengthening causal inference with the fixed-effects approach. Our research has also demonstrated the importance
of psychosocial job quality for the mental health of young workers. Using the US National Longitudinal Survey of Youth (NLSY),
Zimmerman et al (42) has shown that jobs with higher “social and occupational status” are associated with lower depressive symptoms for young
employed males, while physically uncomfortable or dangerous jobs are associated with more depressive symptoms for young women.
Other studies have highlighted the importance of psychosocial job quality on the wellbeing on young people over the course
of their working life (43).

There are a number of factors that need to be taken into consideration in assessing these results. First, our outcome and
exposure variables are self-reported; thus there is a possibility for dependent misclassification (common method variance),
whereby errors in the exposure and outcome are correlated; to the extent the drivers of dependent misclassification (such
as negative affect) are time invariant, they will be controlled for by the fixed-effects approach. In addition to the stressors
contributing to the job quality measure used in this study, there are many other important psychosocial aspects of the work
environment that were not included that could also have an influence on mental health (eg, social support and bullying at
work), suggesting our findings provides a conservative estimate of the influence of workplace psychosocial stressors on mental
health. We were also not able to ascertain other potential confounders, such as the young person’s role in their household,
so we could not accurately measure their living arrangements or capture the transition from living at home with parents to
living with others, which is another potentially important influence on mental health.

As exposure to psychosocial job quality and mental health were modelled contemporaneously in our models (measured in the same
wave), we acknowledge the potential for reverse causality (ie, poor mental health could influence psychosocial job quality).
Previous research assessing the potential for reverse causation between job demands and control and mental health has found
some evidence for reciprocal causal relationships between work characteristics and mental health, but the effects of work
characteristics on mental health were causally dominant (32). Recent research we have conducted also suggests that the relationship between job stressors and mental health in mainly
contemporaneous (22). The sensitivity analysis including lagged effects suggested results in the same direction as the main tables reported in
the manuscript (albeit being non-significant). Finally, there may be differences in the relationship between job quality and
mental health by gender, and thus we would suggest this as an area of future research.

In stating these limitations, there were a number of strengths in this study. These included the ability to examine the relationships
between psychosocial working conditions and mental health over time using a large representative national sample. We were
able to use a previously validated measure of psychosocial job quality. The fixed-effects analytical approach allowed us to
examine causally-robust within-person associations controlling for both measured and unmeasured time-invariant confounders
that may have otherwise biased results even though the estimates obtained, strictly speaking, are generalizable only to those
participants reporting changes in exposure over their contributed waves (and not to the entire source population). Our study
provides a novel contribution to research as it is among the first to assess the relationships between employment, mental
health, and psychosocial quality of jobs among young Australian workers. Specifically, this paper simultaneously assesses
both the potential harmful aspects of working conditions, as well as the benefits of good quality work for mental health.

Work can provide many benefits to life satisfaction, well-being, and the development of resilience, including the promotion
of self-efficacy and self-esteem, a sense of structure and meaning, the development of social connections, support to extend
family and neighborhood networks, and the provision of income (44). Having a healthier workforce also holds the potential to result in better productivity outcomes for employers, and lower
reliance on social welfare. Thus, promoting high quality psychosocial work for younger workers acts to protect and enhance
their well-being, and may subsequently reduce the likelihood of later mental health problems, particularly if this sets the
young person up for a working life characterized by good psychosocial quality jobs. This involves a combination of reducing
the presence of psychosocial job stressors at the same time as promoting the positive aspects of work (44). Addressing both these factors is recognized as the most integrated and long-term beneficial way of improving workplace
mental health.

Acknowledgments

This paper uses unit record data from the HILDA Survey, which was initiated and is funded by the Australian Government Department
of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research Melbourne Institute.
The findings and views reported in this paper, however, are those of the author and should not be attributed to either DSS
or the Melbourne Institute. The data used in this paper was extracted using the Add-On Package PanelWhiz for Stata. PanelWhiz
(http://www.PanelWhiz.eu) was written by Dr. John P. Haisken-DeNew (john@PanelWhiz.eu).

Wilkins, R. (2013). Families, Incomes and Jobs, Volume 8: A Statistical Report on Waves 1 to 10 of the Household, Income and Labour Dynamics in
Australia Survey. Melbourne: Melbourne Institute of Applied Economic and Social Research Faculty of Business and Economics.

Butterworth, P, & Crosier, T. (2004). The validity of the SF-36 in an Australian National Household Survey: demonstrating
the applicability of the Household Income and Labour Dynamics in Australia (HILDA) Survey to examination of health inequalities.
BMC public health, 4(1), 44, https://doi.org/10.1186/1471-2458-4-44.

Yamazaki, S, Fukuhara, S, & Green, J. (2005). Usefulness of five-item and three-item Mental Health Inventories to screen
for depressive symptoms in the general population of Japan. Health and Quality of Life Outcomes, 3, 48, https://doi.org/10.1186/1477-7525-3-48.